Sep 2026· Zenodo (CERN European Organization for Nuclear Research)· 2 citations· 3 references
Abstract
This working paper argues that technical superiority is neither a necessary nor a sufficient condition for the diffusion of a new technology, and that the gap between technical capability and institutional readiness — the organizational, financial, and legal infrastructure required to convert an invention into a durable system — is a more reliable predictor of which technologies succeed. The argument is developed through a single historical case: the "War of the Currents" between Nikola Tesla's and George Westinghouse's alternating-current (AC) system and Thomas Edison's direct-current (DC) system, and the later collapse of Tesla's Wardenclyffe wireless-transmission project. Alternating current prevailed commercially despite Edison's better-funded opposition campaign, because Westinghouse supplied the institutional infrastructure — patent consolidation, standardization, capital access, and legal defense — that Tesla's invention alone could not provide. Wardenclyffe failed for the mirror-image reason: Tesla could not translate technical vision into a financing structure that J. P. Morgan and other investors could underwrite. The paper reframes this historical episode as an early instance of the institutional-readiness gap that the author's ongoing research program documents empirically in contemporary artificial-intelligence governance, and situates it explicitly alongside the author's related working papers on adaptation and human-capital readiness. The paper is conceptual and illustrative rather than causal: it draws on a single, well-documented historical case to motivate a framework, not to test it statistically.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9